Gestational age-specific serum creatinine can predict adverse pregnancy outcomes

Serum creatinine level (SCr) typically decreases during pregnancy due to physiologic glomerular hyperfiltration. Therefore, the clinical practice of estimated glomerular filtration rate (eGFR) based on SCr concentrations might be inapplicable to pregnant women with kidney disease since it does not take into account of the pregnancy-related biological changes. We integrated the Wonju Severance Christian Hospital (WSCH)-based findings and prior knowledge from big data to reveal the relationship between the abnormal but hidden SCr level and adverse pregnancy outcomes. We analyzed 4004 pregnant women who visited in WSCH. Adverse pregnancy outcomes included preterm birth, preeclampsia, fetal growth retardation, and intrauterine fetal demise. We categorized the pregnant women into four groups based on the gestational age (GA)-unadjusted raw distribution (Q1–4raw), and then GA-specific (Q1–4adj) SCr distribution. Linear regression analysis revealed that Q1-4adj groups had better predictive outcomes than the Q1–4raw groups. In logistic regression model, the Q1–4adj groups exhibited a robust non-linear U-shaped relationship with the risk of adverse pregnancy outcomes, compared to the Q1–4raw groups. The integrative analysis on SCr with respect to GA-specific distribution could be used to screen out pregnant women with a normal SCr coupled with a decreased renal function.


Scientific Reports
| (2022) 12:11224 | https://doi.org/10.1038/s41598-022-15450-w www.nature.com/scientificreports/ three groups based on the severity of kidney dysfunction: mild (SCr < 1.5 mg/dL), moderate (1.5-2.5 mg/dL), and severe (> 2.5 mg/dL) 14 . However, Smith et al. 5 suggested that the sole application of SCr concentration or SCr-based equations tend to substantially underestimate renal function during pregnancy. The Torino-Cagliari Observational Study 15 found that the risk of adverse pregnancy outcomes increases stepwise as CKD stage 1 transitions to stages 4-5. However, the risk of adverse pregnancy outcomes was not significantly different between pregnant CKD stage 1 patients and normal pregnant women 5 . Multidisciplinary expert groups in Seoul National University focused on GFR estimated by midterm SCr to identify the association between kidney function and adverse pregnancy outcomes 9 .
Our study had two main tasks. First, we integrated an automatic platform and a manual method to establish a dataset on pregnant women. The automatic work was performed by a laboratorian, a computer scientist, and a database administrator, and the manual task was performed by domain experts, including an obstetrician and a family medicine doctor. Second, we implemented prior knowledge 16 to improve the predictive power of adverse pregnancy outcomes. Recently, Harel, Ray and their colleagues 8,16 collected approximately 362,000 measurements of SCr levels from 243,534 women, and established the GA-specific distribution. In this study, we applied the previously validated findings to identify the association between kidney function and adverse pregnancy outcomes.

Results
Determination of SCr group based on the GA-specific SCr distribution. We analyzed 4004 pregnant women who visited Wonju Severance Christian Hospital (WSCH) during the study period. For the 4004 subjects, 10,126 measurements of SCr levels existed, indicating that a person had an average of 2.53 times of SCr evaluations. In most cases (n = 1942), the SCr was measured once; it was measured 23 times in two cases and 24 times in one case (Fig. 1). For patients with multiple SCr measurements, we used the maximum estimated value of SCr level.
We used two methods for grouping all pregnancies based on the level of SCr (Fig. 2). At first, we divided SCr into four groups based on the 25th, 50th, and 75th percentile of SCr levels ( Fig. 2A), thereby, 1097, 978, 955, and 974 subjects were evenly grouped into four raw groups by SCr levels [quartile (Q)1 raw to Q4 raw, respectively]. For the other method, we grouped all SCr results according to the distribution of GA-specific SCr levels, as proposed by Harel and colleagues 16 . In detail, we mapped our SCr results onto this distribution that includes GA and SCr levels as X and Y axes, respectively (Fig. 2B). As a result, 1,888, 813, 835, and 468 subjects were arranged into four groups [Q1 adj (an adjusted group by GA-specific Scr distribution) to Q4 adj , respectively].
Medical and biochemical characteristics. General characteristics, including medical and biochemical data based on the four SCr groups determined by the GA-specific SCr distribution 16 are summarized in Table 1. The average SCr levels showed a stepwise increase as it transitioned from the Q1 adj to Q4 adj group. The mean age was lowest in Q1 adj group, but did not significantly differ among Q2 adj to Q4 adj groups ( Table 1). The subjects in the Q4 adj group had the following characteristics when compared to the Q1 adj group: high ratio of multi-para, diabetes, preterm birth (PTB), preeclampsia, and fetal growth retardation (FGR) ( Table 1). www.nature.com/scientificreports/ Associational patterns of two grouping methods based on different SCr distributions with adverse pregnancy outcomes. The SCr groups stratified based on raw SCr distribution (Q1 raw -Q4 raw ) could reflect the adverse pregnancy outcomes. In detail, the number of cases with two or more adverse pregnancy outcomes significantly increased from Q1 raw to Q4 raw . However, the ratio of cases with two or more number of adverse pregnancy outcomes increased gradually from Q1 raw to Q3 raw , but drastically from Q3 raw to Q4 raw (Fig. 3A). The four groups made by the GA-specific SCr distribution showed a more gradual pattern of increase in cases with an adverse pregnancy outcomes (Fig. 3B), compared to the original SCr distribution. For the cases with two or more adverse outcomes, two grouping methods showed a well-characterized stepwise increase pattern (Fig. 3A,B). In uni-or multivariate linear regression (LiR) with SCr group and the number of adverse pregnancy outcomes set as independent and dependent variables, the GA-specific SCr group had high betacoefficients and low p-values, compared to the groups determined by the original SCr distribution (Fig. 3C).
We re-grouped all subjects into two groups (with or without an adverse pregnancy outcomes), then conducted uni-or multivariate logistic regression (LR) considering the binary status (adverse pregnancy outcomes vs. control) and quartile group of SCr (Q1-Q4 raw or Q1-Q4 adj ) as dependent and independent variables, respectively. Both original and GA-specific SCr distributions had non-linear U shaped associations with adverse pregnancy outcomes, but the results in Q3 raw and Q4 raw showed insignificant results (Fig. 4A). The GA-specific SCr groups showed a significant U-shaped relationship with the risk for adverse pregnancy outcomes (Fig. 4A). The relational pattern between SCr and PTB was similar with that of all types of adverse pregnancy outcomes (Fig. 4B). In cases of preeclampsia and FGR, the SCr level exhibited a monotonic increasing pattern of relationship (Fig. 4C,D). For intrauterine fetal demise (IUFD), Q1 adj and Q4 adj had a significantly increased risk (Fig. 4E).

Discussion
In this retrospective study, we identified that SCr level could predict the risk of adverse pregnancy outcomes as well as the number of co-occurred adverse pregnancy outcomes. Specifically, when using the GA-specific SCr distribution 16 , the predictive power of adverse pregnancy outcomes was more robust based on the betacoefficients and their p-values, compared to the raw SCr distribution (Fig. 3). Moreover, we identified that SCr   www.nature.com/scientificreports/ groups (Q1 adj -Q4 adj ) determined by the GA-specific SCr distribution exhibited a non-linear U-shaped relationship with the risk of adverse pregnancy outcomes. Park et al. 9 reported the convergent findings with our results that midterm GFR had a non-linear relationship with the adverse pregnancy outcomes. Several studies have pinpointed that exclusively using SCr-based equations could result in the misclassification of kidney function during pregnancy 5,16 . To overcome this limitation, Park et al. 9 only analyzed the midterm GFR values estimated based on SCr levels and their relationship with adverse pregnancy outcomes. Moreover, Harel et al. 16 collected approximately 362,000 SCr results from about 240,000 women and generated the GA-specific SCr distribution. We could not find the GA-specific GFR distribution and could only obtain the GA-specific SCr distribution 16 , so we designed the linear regression or logistic regression models based on SCr levels, not GFR values. Direct application of the findings obtained from the Canadian population to the Korean pregnant women population may yield inaccurate results due to genetic and ethnic differences. However, we considered this distribution to be generalized, and not biased results because it was obtained from over 360,000 results 16 . In addition, it was obvious to provide an improved prediction of the adverse pregnancy outcomes of Korean women www.nature.com/scientificreports/ when using the Canadian-based distribution. Future studies to curate Korean-specific SCr distribution based on GA are needed. Moreover, it is crucial to establish the GA-specific GFR distribution. SCr value is dependent on muscle mass or body composition, such as age, sex, and ethnicity 17 . Due to many factors adversely impacting the accurate estimation of GFR, Kidney Disease Improving Global Outcomes (KDIGO) guidelines recommend confirming CKD status in a specific population initially determined based on GFR estimated by SCr (eGFR Cr ) using an alternative method, such as GFR estimated by serum cystatin C (eGFR cys) 18 . Our study also focused on the inaccurate estimation of kidney function measured by SCr in a specific population (ie, pregnant women), and suggested that the combined information of SCr level and gestational age-specific SCr distribution 16 could improve the prediction of adverse pregnancy outcomes. In pregnancy, albeit kidney hyperfiltration, the maternal serum level of cystatin C is known to be more stable than SCr before the third trimester 19 . Serum level of cystatin C is known to be elevated in the third trimester of pregnancy; however, its underlying mechanism is not converged [19][20][21] . In general population, cumulative evidence has supported that eGFR cys is a good alternative method for the GFR estimation, specifically in subgroup exhibiting low muscle amount: elderly population, vegetarians, and those with muscle wasting, chronic disease, or limb amputation 22 . Moreover, most studies commonly suggested that a combined creatinine and cystatin C equation proposed more accurate estimates and greater precision than GFR estimated from creatinine or cystatin C alone 22,23 . Taken together, we suggest that the kidney function of pregnant women is to be measured via multiple serum indices, such as creatinine and cystatin C.
Adverse pregnancy outcomes, such as preeclampsia and PTB, have been considered the leading causes of perinatal morbidity and mortality worldwide 24,25 . Therefore, establishing a prediction model for adverse pregnancy outcomes is crucial to help minimize adverse perinatal outcomes 26,27 . The establishment of the prediction model included two main tasks: one is the feature selection and the other is the optimization of parameters using several machine learning methods 28 . The present study focused on feature selection. Moreover, our results were obtained from previously validated findings 16 , also called prior knowledge, indicating that our study analyzed data based on the Bayesian approach or transfer learning 29 . Our findings indicated a non-linear relationship between SCr levels and adverse pregnancy outcomes; therefore, machine learning methods (e.g., support vector machine, random forest, or deep learning) 30 that can deal with data exhibiting characteristics of the non-linear or complex relationships among features 30 are needed to screen patients with high risk adverse pregnancy outcomes.
In the present study, multidisciplinary experts (i.e., laboratorian, database administrator, obstetrician, and computer scientist) performed multiple tasks to construct a database to identify the risk factors for adverse pregnancy outcomes. Initially, obstetricians are responsible for the literature-based review to select candidate features. Later, laboratorian, database administrator, and computer scientist constructed an initial dataset using the automatic platform, including the candidate variables. Then, the obstetrician and laboratorian manually updated the patient's medical history and pregnancy outcomes.
This study has several limitations. First, because the WSCH is a tertiary hospital, the prevalence of pregnant women with adverse pregnancy outcomes is high compared to general population. To obtain generalized findings, the multi-institutional study or registry is needed, and this study could motivate the multi-center approach. Second, several biomarkers could not be analyzed since the database (e.g., electronic health records and automatic platform) is not designed to compile them. For example, we could not analyze data, such as body mass index (BMI) or waist circumference because they were not recorded at the time of initial pregnancy evaluation. Moreover, due to the retrospective design, we could not control the re-examination of blood tests of pregnant women, resulting that most women examined blood only had a time point of measurement (Fig. 1). Future study that is reinforced with these limitations could identify the valuable pathophysiological signatures related to decreased renal function and blunted hyperfiltration, and adverse pregnancy outcomes.
We implemented prior knowledge 6 obtained from the enormously large scale of data and observed that elevated SCr levels were significantly related with the risk of adverse pregnancy outcomes. This study is a basic task of developing an algorithm to predict the possibility of adverse pregnancy outcomes based on the pregnant women's SCr levels adjusted by gestational weeks.

Data collection.
We performed a retrospective study on pregnant women giving birth between January 2010 and December 2020 at the WSCH. Eligible subjects were women who had a singleton birth at more than 20 weeks gestation and were aged 16-50 years at the time of delivery. We constructed the initial dataset with the above criterion (i.e., 20 weeks and age) by database administrator as our previous work 29 . Afterward, we selected pregnancy cases based on patients' case notes that had been manually recorded by the department of Obstetrics and Gynecology. We excluded pregnant women that did not have data for SCr levels and SCr measurement times.
The present study was approved by the Institutional Review Boards (IRB) of WSCH (CR321084). This study was conducted in accordance with the principles of the Declaration of Helsinki. The study was an observational study without medical intervention, so the need to obtain informed consent from patients was waived. The waiver of informed consent was also confirmed by the above IRB of WSCH (CR321084).

Adverse pregnancy outcomes.
Several studies have analyzed the association between kidney function and adverse pregnancy outcomes 8,16 . Park et al. 7 defined pregnancy outcomes as being adverse when a pregnant woman has one or more of the following: PTB, low birth weight, or preeclampsia. Harel et al. 14 selected extreme PTB, preeclampsia, perinatal mortality, and severe FGR as the main adverse pregnancy outcomes. Motivated by these studies, we determined PTB (delivery before 37 weeks gestation), preeclampsia (blood pressure ≥ 140/90 www.nature.com/scientificreports/ with significant proteinuria and/or end organ damage), FGR (birthweight below 10th percentile adjusted for gestational age), and IUFD as adverse pregnancy outcomes.
Covariates. Harel et al. 8 determined maternal age, rural residence, region of origin, gestational week, preconception SCr, chronic diseases (diabetes and hypertension), and tobacco/illicit drug (each ≤ years before conception) as covariates. Park et al. 7 selected age, BMI, weight gain until delivery, hypertension, diabetes, hypertensive disorder during pregnancy, previous history of stillbirth/miscarriage, multiparity, and hospitalization status. Motivated by these studies, we categorized the parity type into two groups: nulliparity and multiparity. For underlying maternal diseases, hypertension and diabetes were included in this study, which were determined only by diagnosis code (i.e., ICD10) due to lack of data pertaining to blood pressure or serum fasting glucose at the time of SCr measurement. SCr measurement was performed using the Cobas® 8000 system (Cobas® c 702 and e 601 module; Roche Diagnostics, Switzerland).